A number of multi-objective genetic algorithms (MOGAs) have been developed to obtain Pareto optimal solutions for multi-objective optimization problems. However, as these methods involve probabilistic algorithms, there is no guarantee that the global search will be conducted in the design variable space. In such cases, there are unsearched areas in the design variable space, and the obtained Pareto solutions may not be truly optimal. In this paper, we propose an optimization method called

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... CT-GA to conduct a global search over as much as possible of the design variable space, which improves the reliability of the obtained Pareto solutions. The effectiveness of NSDIRECT-GA was examined through numerical experiments. By NSDIRECT-GA, not only the optimal solutions but also the information of the landscape can be determined, and it is possible to obtain Pareto solutions with higher reliability than with normal MOGAs.